COMMUNITY STRUCTURE, a significant and useful statistical
characteristic, is ubiquitous in social networks.
17 Based
on it, a network can be viewed as consisting of multiple
units. The nodes (users) are highly connected to each
other inside a unit, while the connections between
units are sparse.
4, 17 For example, people with similar
interests or backgrounds might join
together to form a community or web-pages with related topics might cluster
together. Different types of information, including rumors,
5 virus attacks,
10
and even cyber epidemics diffuse
through social networks,
8 possibly leading to unexpected social effects. A typical example is the worldwide cyberat-tack by WannaCry ransomware, as first
reported May 12, 2017, that resulted
in the infections of more than 200,000
organizations worldwide.
15 The underlying attack reflects a malicious diffusion in the presence of communities;
that is, the homogeneous feature of
individuals leads to the community’s
vulnerability. It is against this backdrop that understanding the potential
dynamics could help network administrators gain insight into controlling
unwanted information diffusion. Much
research today involves networks with
community structure (such as to detect
Even Central
Users Do Not
Always Drive
Information
Diffusion
DOI: 10.1145/3224203
Diffusion speed and scale depend on
all kinds of information, not just which users
have the most or fewest connections.
BY CHAO GAO, ZHEN SU, JIMING LIU, AND JÜRGEN KURTHS
